Addressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic

• Published in: Population health metrics Vol. 19; no. 1; pp. 1 - 44
• Main Authors: Feng, Shuo, Hategeka, Celestin, Grépin, Karen Ann
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 04.11.2021; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Bias; Congo (Kinshasa); COVID-19; Data mining; Data structures; Datasets; Epidemics; Errors; Estimates; Health care facilities; Health management information system (HMIS); Health services research; Information management; Information systems; Interpolation; Learning algorithms; Low- and middle-income countries (LMICs); Machine learning; Management information systems; Mathematical analysis; Medical informatics; Methods; Missing data; Missing observations (Statistics); Multiple imputation; Pandemics; Parameter estimation; Performance evaluation; Regression analysis; Routine health information systems (RHIS); Stability analysis; Statistics; Congo (Kinshasa); Congo-Democratic Republic of Congo
• ISSN: 1478-7954; 1478-7954
• DOI: 10.1186/s12963-021-00274-z

Poor data quality is limiting the use of data sourced from routine health information systems (RHIS), especially in low- and middle-income countries. An important component of this data quality issue comes from missing values, where health facilities, for a variety of reasons, fail to report to the central system. Using data from the health management information system in the Democratic Republic of the Congo and the advent of COVID-19 pandemic as an illustrative case study, we implemented seven commonly used imputation methods and evaluated their performance in terms of minimizing bias in imputed values and parameter estimates generated through subsequent analytical techniques, namely segmented regression, which is widely used in interrupted time series studies, and pre-post-comparisons through paired Wilcoxon rank-sum tests. We also examined the performance of these imputation methods under different missing mechanisms and tested their stability to changes in the data. For regression analyses, there were no substantial differences found in the coefficient estimates generated from all methods except mean imputation and exclusion and interpolation when the data contained less than 20% missing values. However, as the missing proportion grew, k-NN started to produce biased estimates. Machine learning algorithms, i.e. missForest and k-NN, were also found to lack robustness to small changes in the data or consecutive missingness. On the other hand, multiple imputation methods generated the overall most unbiased estimates and were the most robust to all changes in data. They also produced smaller standard errors than single imputations. For pre-post-comparisons, all methods produced p values less than 0.01, regardless of the amount of missingness introduced, suggesting low sensitivity of Wilcoxon rank-sum tests to the imputation method used. We recommend the use of multiple imputation in addressing missing values in RHIS datasets and appropriate handling of data structure to minimize imputation standard errors. In cases where necessary computing resources are unavailable for multiple imputation, one may consider seasonal decomposition as the next best method. Mean imputation and exclusion and interpolation, however, always produced biased and misleading results in the subsequent analyses, and thus, their use in the handling of missing values should be discouraged.